Publication: Fully automated breast segmentation on spiral breast computed tomography images
Fully automated breast segmentation on spiral breast computed tomography images
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Shim, S., Cester, D., Ruby, L., Bluethgen, C., Marcon, M., Berger, N., Unkelbach, J., & Boss, A. (2022). Fully automated breast segmentation on spiral breast computed tomography images. Journal of Applied Clinical Medical Physics, 23(10), e13726. https://doi.org/10.1002/acm2.13726
Abstract
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Abstract
INTRODUCTION: The quantification of the amount of the glandular tissue and breast density is important to assess breast cancer risk. Novel photon-counting breast computed tomography (CT) technology has the potential to quantify them. For accurate analysis, a dedicated method to segment the breast components-the adipose and glandular tissue, skin, pectoralis muscle, skinfold section, rib, and implant-is required. We propose a fully automated breast segmentation method for breast CT images.
METHODS: The framework consists of four parts
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Creators (Authors)
- Shim, Sojin
- Ruby, Lisa
- Bluethgen, Christian
- Marcon, Magda
- Berger, Nicole
- Unkelbach, Jan
- Boss, Andreas
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Shim, S., Cester, D., Ruby, L., Bluethgen, C., Marcon, M., Berger, N., Unkelbach, J., & Boss, A. (2022). Fully automated breast segmentation on spiral breast computed tomography images. Journal of Applied Clinical Medical Physics, 23(10), e13726. https://doi.org/10.1002/acm2.13726
